- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Monitoring Land-Use/Land-Cover Changes at a Provincial Large Scale Using an Object-Oriented Technique and Medium-Resolution Remote-Sensing Images
摘要： An object-based image analysis (OBIA) technique is replacing traditional pixel-based methods and setting a new standard for monitoring land-use/land-cover changes (LUCC). To date, however, studies have focused mainly on small-scale exploratory experiments and high-resolution remote-sensing images. Therefore, this study used OBIA techniques and medium-resolution Chinese HJ-CCD images to monitor LUCC at the provincial scale. The results showed that while woodland was mainly distributed in the west, south, and east mountain areas of Hunan Province, the west had the largest area and most continuous distribution. Wetland was distributed mainly in the northern plain area, and cultivated land was distributed mainly in the central and northern plains and mountain valleys. The largest impervious surface was the Changzhutan urban agglomerate in the northeast plain area. The spatial distribution of land cover in Hunan Province was closely related to topography, government policy, and economic development. For the period 2000–2010, the areas of cultivated land transformed into woodland, grassland, and wetland were 183.87 km2, 5.57 km2, and 70.02 km2, respectively, indicating that the government-promoted ecologically engineered construction was yielding some results. The rapid economic growth and urbanization, high resource development intensity, and other natural factors offset the gains made in ecologically engineered construction and in increasing forest and wetland areas, respectively, by 229.82 km2 and 132.12 km2 from 2000 to 2010 in Hunan Province. The results also showed large spatial differences in change amplitude (LUCCA), change speed (LUCCS), and transformation processes in Hunan Province. The Changzhutan urban agglomerate and the surrounding prefectures had the largest LUCCA and LUCCS, where the dominant land cover accounted for the conversion of some 189.76 km2 of cultivated land, 129.30 km2 of woodland, and 6.12 km2 of wetland into impervious surfaces in 2000–2010. This conversion was attributed to accelerated urbanization and rapid economic growth in this region.
关键词： change monitoring,object-based image analysis,provincial scale,HJ-CCD images
更新于2025-09-23 15:23:52
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[IEEE 2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO) - Kiev (2018.4.24-2018.4.26)] 2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO) - A Very High Resolution Satellite Imagery Classification Algorithm
摘要： This work is devoted to high-resolution WorldView-2 and WorldView-3 satellite imagery processing. We have developed a satellite imagery automatic classification algorithm based on the object-based approach. Three different segmentation methods are investigated in order to determine which is the most appropriate for our task. The experimental results show a good accuracy of the proposed algorithm.
关键词： image features,classification,segmentation,Kappa index,object-based image analysis
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP) - Shenzhen, China (2018.7.13-2018.7.15)] 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP) - An Object-Based Method Based on a Novel Statistical Distance for SAR Image Change Detection
摘要： This paper introduces an object-based method based on a new statistical distance for SAR image change detection. Firstly, multi-temporal segmentation is carried out to segment two temporal SAR images simultaneously. It considers the homogeneity in two temporal images, and could generate homogeneous objects in spectral, spatial and temporal. In addition, through setting different segmentation parameters, the multi-temporal images can be segmented in a set of scales. This process exploits the advantages of OBIA that could effectively reduce spurious changes, and considers the scale of change detection task. Secondly, a multiplicative noise model called Nakagami–Rayleigh distribution is employed to describe SAR data, and then applied to Bayesian formulation. Thus, a new statistical distance that is insensitive to speckles is derived to measure the distances between pairs of parcels. Then, cluster ensemble algorithm is utilized to improve accuracy of individual result in each scale to obtain the final change detection map. Finally, multi-temporal Radarsat-2 images are employed to verify the effectiveness of the proposed method compared with other four methods.
关键词： synthetic aperture radar (SAR),multi-scale analysis,object-based image analysis,change detection
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Integration of SAR and GEOBIA for the Analysis of Time-Series Data
摘要： In this work, we present a new architecture for the analysis multitemporal SAR data combining classic synthetic aperture radar processing and geographical object-based image analysis. The architecture exploits the characteristics of the recently introduced RGB products of the Level-1α and Level-1β families, employing self-organizing map clustering and object-based image analysis aiming at the definition of opportune layers measuring scattering and geometric properties of candidate objects to classify. The obtained results have been compared with those given by literature and turned out to provide high degree of accuracy and negligible false alarms. The discussion is supported by an example concerning small reservoir mapping in semi-arid environment.
关键词： self-organizing map clustering,classification,object-based image analysis,multitemporal synthetic aperture radar
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Very High Resolution Optical Image Classification Using Watershed Segmentation and a Region-Based Kernel
摘要： In this paper, the problem of the spatial-spectral classification of very high-resolution optical images is addressed using a kernel- and region-based approach. A novel method based on integrating region-based or object-based information into a kernel machine is developed. A Gaussian process model is used to characterize each segment in a segmentation map and to define a region-based admissible kernel accordingly. This kernel is combined with a marker-controlled watershed segmentation that incorporates scale adaptivity. Spatial-spectral fusion capabilities are also ensured by combining the resulting classification method with composite kernels.
关键词： watershed segmentation,region-based classification,Kernel machines,geospatial object-based image analysis (GEOBIA)
更新于2025-09-23 15:21:21
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Feature-Level Fusion of Landsat 8 Data and SAR Texture Images for Urban Land Cover Classification
摘要： Each of the urban land cover types has unique thermal pattern. Therefore, thermal remote sensing can be used over urban areas for indicating temperature differences and comparing the relationships between urban surface temperatures and land cover types. On the other hand, synthetic-aperture radar (SAR) sensors are playing an increasingly important role in land cover classi?cation due to their ability to operate day and night through cloud cover, and capturing the structure and dielectric properties of the earth surface materials. In this research, a feature-level fusion of SAR image and all bands (optical and thermal) of Landsat 8 data is proposed in order to modify the accuracy of urban land cover classi?cation. In the proposed object-based image analysis algorithm, segmented regions of both Landsat 8 and SAR images are utilized for performing knowledge-based classi?cation based on the land surface temperatures, spectral relationships between thermal and optical bands, and SAR texture features measured in the gray-level co-occurrence matrix space. The evaluated results showed the improvements of about 2.48 and 0.06 for overall accuracy and kappa after performing feature-level fusion on Landsat 8 and SAR data.
关键词： Thermal remote sensing,SAR data,Object-based image analysis,Textural features,Feature-level fusion
更新于2025-09-23 15:21:21
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Another look on region merging procedure from seed region shift for high-resolution remote sensing image segmentation
摘要： Region merging method is widely used for remote sensing image segmentation in Geographic Object-Based Image Analysis (GEOBIA) because of its simplicity and effectiveness. Instead of improving the merging strategy, similarity measure, and stopping rule for region merging method as usual, we aim at exploring the effectiveness of the seed region shift on region merging-based segmentation. Different region merging procedures with different seed region shift frequencies are compared by fixing other conditions, demonstrating that the shift of seed regions serves as one of the key impacts to segmentation accuracy for region merging method. If the seed regions keep fixed during region merging procedure, it will lead to uneven expansion of regions and consequently low segmentation accuracy. However, if the seed regions can be dynamically shifted during region merging procedure, it will lead to even expansion of regions and achieve similar segmentation performance for different region merging strategies. The findings could be beneficial to selecting or further improving image segmentation method for GEOBIA.
关键词： High-resolution remote sensing,Geographic object-based image analysis,Region merging,Seed region,Image segmentation
更新于2025-09-19 17:15:36
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Object-Based Image Procedures for Assessing the Solar Energy Photovoltaic Potential of Heterogeneous Rooftops Using Airborne LiDAR and Orthophoto
摘要： Available renewable energy resources play a vital role in fulfilling the energy demands of the increasing global population. To create a sustainable urban environment with the use of renewable energy in human habitats, a precise estimation of solar energy on building roofs is essential. The primary goal of this paper is to develop a procedure for measuring the rooftop solar energy photovoltaic potential over a heterogeneous urban environment that allows the estimation of solar energy yields on flat and pitched roof surfaces at different slopes and in different directions, along with multi-segment roofs on a single building. Because of the complex geometry of roofs, very high-resolution data, such as ortho-rectified aerial photography (orthophotos), and LiDAR data have been used to generate a new object-based algorithm to classify buildings. An overall accuracy index and a Kappa index of agreement (KIA) of 97.39% and 0.95, respectively, were achieved. The paper also develops a new model to create an aspect-slope map, which combines slope orientation with the gradient of the slope and uses it to demonstrate the collective results. This study allows the assessment of solar energy yields through defining solar irradiances in units of pixels over a specific time period. It might be beneficial in terms of more efficient measurements for solar panel installations and more accurate calculations of solar radiation for residents and commercial energy investors.
关键词： orthophoto,solar energy photovoltaic potential,object-based image analysis,aspect-slope map,segmentation,LiDAR
更新于2025-09-19 17:13:59
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Experimental Multiphase Estimation in an Integrated Reconfigurable Multi-Arm Interferometer
摘要： The main objective of this research was to establish a semiautomated object-based image analysis (OBIA) methodology for locating landslides. We have detected and delineated landslides within a study area in north-western Iran using normalized difference vegetation index (NDVI), brightness, and textural features derived from satellite imagery (IRS-ID and SPOT-5) in combination with slope and ?ow direction derivatives from a digital elevation model (DEM) and topographically oriented gray-level cooccurrence matrices (GLCMs). We utilized particular combinations of these information layers to generate objects by applying multiresolution segmentation in a sequence of feature selection and object classi?cation steps. The results were validated by using a landslide inventory database including 109 landslide events. In this study, a combination of these parameters led to a high accuracy of landslide delineation yielding an overall accuracy of 93.07%. Our results con?rm the potential of OBIA for accurate delineation of landslides from satellite imagery and, in particular, the ability of OBIA to incorporate heterogeneous parameters such as DEM derivatives and surface texture measures directly in a classi?cation process. The study contributes to the establishment of geographic object-based image analysis (GEOBIA) as a paradigm in remote sensing and geographic information science.
关键词： object-based image analysis (OBIA),textural rule-based classi?cation,gray-level cooccurrence matrix (GLCM),landslide mapping,remote sensing,GIScience
更新于2025-09-16 10:30:52
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Object-based correction of LiDAR DEMs using RTK-GPS data and machine learning modeling in the coastal Everglades
摘要： Light Detection and Ranging (LiDAR) Digital Elevation Models (DEMs) are frequently applied in modeling coastal environments. We present an object-based correction approach for accurate and precise DEMs by integrating LiDAR point data, aerial imagery, and Real Time Kinematic-Global Positioning Systems. Four machine learning techniques (Random Forest, Support Vector Machine, k-Nearest Neighbor, and Artificial Neural Network) were compared with the commonly used bias-correction method. The Random Forest object-based model produced best predictions for two study areas: Nine Mile (Mean Bias Error (MBE) reduced 0.18 to ?0.02 m, Root Mean Square Error (RMSE) reduced 0.22 to 0.08 m) and Flamingo (MBE reduced 0.17 to 0.02 m, RMSE reduced 0.24 to 0.10 m). A Monte Carlo model was developed to combine errors into the object-based machine learning corrected DEMs, and uncertainty maps spatially revealed the likelihood of error. The object-based correction approach provides an attractive alternative to the bias-correction method.
关键词： DEMs,Object-based image analysis,Monte Carlo,LiDAR,Machine learning,Coastal wetlands
更新于2025-09-10 09:29:36